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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2008, 13(3), 153-163; https://doi.org/10.3390/mca13030153

Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms

Department of Electronics and Computer Education, Selçuk University, 42003, Konya, Turkey
Published: 1 December 2008
PDF [196 KB, uploaded 31 March 2016]

Abstract

This paper presents a multiobjective fuzzy genetic algorithm optimization approach to design the submersible induction motor with two objective functions: the full load torque and the manufacturing cost. A multiobjective fuzzy optimization problem is formulated and solved using a genetic algorithm. The optimally designed motor is compared with an industrial motor having the same ratings. The results of optimal design show the reduction in the manufacturing cost, and the improvement in the full load torque of the motor.
Keywords: Multiobjective Fuzzy Optimization; Submersible Induction motor; Genetic Algorithms Multiobjective Fuzzy Optimization; Submersible Induction motor; Genetic Algorithms
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Çunkaş, M. Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms. Math. Comput. Appl. 2008, 13, 153-163.

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